Prediction of Daily Network Traffic based on Radial Basis Function Neural Network

2016 ◽  
Vol 3 (4) ◽  
pp. 145
Author(s):  
Haviluddin Haviluddin ◽  
Imam Tahyudin
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Traffic ◽  
Good Method ◽  
Series Data ◽  
Percentage Error ◽  
Absolute Deviation ◽  
East Kalimantan ◽  
Radial Basis

This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21 – 24 June 2013 (192 samples series data) in ICT Unit Universitas Mulawarman, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of percentage error (MPE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic.

scholarly journals Time Series Prediction Using Radial Basis Function Neural Network

2015 ◽  
Vol 5 (4) ◽  
pp. 765 ◽  
Author(s):  
Haviluddin Haviluddin ◽  
Imam Tahyudin
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Traffic ◽  
Time Series Prediction ◽  
Good Method ◽  
Series Data ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Radial Basis

This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21-24 June 2013 (192 samples series data) in ICT Unit of Mulawarman University, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic.

scholarly journals PERAMALAN JUMLAH TITIK PANAS PROVINSI KALIMANTAN TIMUR MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK

2021 ◽  
Vol 2 (2) ◽  
pp. 64-74
Author(s):  
SITI AISYAH ◽  
SRI WAHYUNINGSIH ◽  
FDT AMIJAYA
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Gaussian Function ◽  
Processing System ◽  
Training Data ◽  
Percentage Error ◽  
Base Function ◽  
East Kalimantan ◽  
Radial Basis

Radial Basis Function Neural Network (RBFNN) is a neural  that uses a radial base function in hidden layers for classification and forecasting purposes. Neural Network is developed into a radial function base with an information processing system that has characteristics similar to biological neural networks, consisting of input layers, hidden layers, and output layers. The data used in this study is data on the number of hotspots in East Kalimantan Province obtained from the official website of the National Aeronautics and Space Administration (NASA). The purpose of this research is to obtain the RBFNN model and the results of forecasting the number of hotspots for the period January 2020 to March 2020. The radial basis function used is the local Gaussian function and the linear activation function. In this study using the proportion of training data and testing data 70: 30; 80:20; and 90:10. The results showed that the input network using significant Partial Autocorrelation Function (PACF) at lag 1 and lag 2, so that the RBFNN model that was formed involved Xt-1 and Xt-2. The best Mean Absolute Percentage Error (MAPE) minimum obtained  the 80:20 data proportion with 2 hidden networks. The RBFNN architecture that is formed is 2 input layers, 2 hidden layers and 1 output layer. Data from forecasting the number of hotspots in East Kalimantan Province shows that from January 2020 to February 2020 there was a decline and March 2020 an increase.

scholarly journals Prediksi Kecepatan Angin Menggunakan Adaptive Neuro Fuzzy (ANFIS) dan Radial Basis Function Neural Network (RBFNN)

2018 ◽  
Vol 4 (1) ◽  
pp. 70
Author(s):  
Nerfita Nikentari ◽  
Martaleli Bettiza ◽  
Helen Sastypratiwi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Time Series Data ◽  
Fuzzy Inference ◽  
Series Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Radial Basis

Angin sebagai salah satu fenomena alam yang mempengaruhi berbagai aspek dalam kehidupan manusia baik pengaruh positif maupun negatif. Aspek ini berperan besar dalam ekonomi, pariwisata, pembangunan, transportasi maupun perdagangan masyarakat. Data angin dalam hal ini kecepatan angin belum dapat diketahui secara pasti nilainya oleh karena itu perlu adanya prediksi. Adaptive Neuro Fuzzy Inference System (ANFIS) dan Radial Basis Function Neural Networkc(RBFNN) adalah algoritma yang dapat digunakan untuk prediksi data. Penelitian ini  menggunakan ANFIS dan RBFNN untuk memprediksi kecepatan angin. Data prediksi yang digunakan dalam penelitian ini adalah data time series. Data kecepatan angin diperoleh dari BMKG (Badan Meteorologi Klimatogi dan Geofisika) Tanjungpinang, Kepualuan Riau. Hasil prediksi dengan kedua metode ini dibandingan dengan data asli untuk mengetahui metode mana yang lebih akurat dalam prediksi data. Hasil pengujian menggunakan kedua algoritma memperlihatkan akurasi terbaik (paling mendekati data asli/target) diperoleh oleh RBFNN yaitu dengan nilai RMSE adalah 0,1766 dan hasil RMSE ANFIS adalah 1,1456.

scholarly journals PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DENGAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK MENGGUNAKAN GUI MATLAB

2018 ◽  
Vol 7 (4) ◽  
pp. 431-442
Author(s):  
Rizki Brendita Br Tarigan ◽  
Hasbi Yasin ◽  
Alan Prahutama
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Radial Basis Function ◽  
Capital Market ◽  
Basis Function ◽  
Time Series Data ◽  
Composite Index ◽  
Computational Method ◽  
Series Data ◽  
Radial Basis

Capital market Indonesia is one of the important factors in the development of the national economy, proved to have many industries and companies that use these institutions as a medium to absorb investment to strengthen its financial position. The recent years, Jakarta Composite Index (JCI) in Capital Market tend to strengthen. JCI data are the time series data obtained from the past to predict the future with caracteristics of JCI data are non stationary and non linier. Neural network is a computational method that imitate the biological neural network. There are several types of methods that can be used in neural network that is: Radial Basis Function Neural Network (RBFNN) Generalized Regression Neural Network (GRNN), dan Probabilistic Neural Network (PNN). Model of Radial Basis Function Neural Network is suitable for time series data. This model has a network architecture in the form of input layer, hidden layer and output layer. This research is done with the help of GUI as a computation tool. The results of analysis by using GUI conducted on the size sample of data as much as 1211 taken as 100 the data thus obtained value of 2315,6 MSE training and training MAPE value of 0,72%, while for the testing of 28886,7 MSE and MAPE testing value is 0,70%. Based on the results of forecasting, JCI values on January 02, 2018 until January 08, 2018 at 6499,922 every day. Keywords: Radial Basis Function Neural Network (RBFNN), Jakarta Composite Index (JCI), MSE, MAPE, Time Series, GUI.

scholarly journals Radial basis function neural network for 2 satisfiability programming

2020 ◽  
Vol 18 (1) ◽  
pp. 459 ◽  
Author(s):  
Shehab Alzaeemi ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam ◽  
Mustafa Mamat
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Squared Error ◽  
Training Technique ◽  
Radial Basis ◽  
Hidden Neurons

<span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.</span>

scholarly journals Optimasi Fuzzy Artificial Neural Network dengan Algoritma Genetika untuk Prediksi Harga Crude Palm Oil

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Anwar Rifa'i
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Palm Oil ◽  
Basis Function ◽  
Time Series Data ◽  
Training Data ◽  
Series Data ◽  
Crude Palm Oil ◽  
Testing Data ◽  
Radial Basis

Crude Palm Oil (CPO) is one of Indonesia's best export commodities. CPO production competition causes price fluctuations so that it can trigger losses. The solution that can be taken to avoid losses is to predict the price of CPO. Time series data in the previous months, starting from January 2009 until January 2020, are used as a reference to predict the next CPO price. In this research, CPO price prediction is carried out with a combination of artificial intelligence concepts, namely Radial Basis Function Neural Network (RBFNN), and fuzzy logic. The combination of these methods, namely Fuzzy Radial Basis Function Neural Network (FRBFNN), is then optimized using genetic algorithms. The prediction results show that the error based on the MAPE value for FRBFNN prediction on training data is 11.7% and the MAPE value for testing data is 9.4%. In the FRBFNN prediction that was optimized using a genetic algorithm, the MAPE value was 10.2% for training data and 8.3% for testing data.

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